Perspectives on Optimizing Care of Patients in Multidisciplinary Chronic Kidney Disease Clinics
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize a jointly held symposium by the Canadian Society of Nephrology (CSN), the Canadian Association of Nephrology Administrators (CANA), and the Canadian Kidney Knowledge Translation and Generation Network (CANN-NET) entitled "Perspectives on Optimizing Care of Patients in Multidisciplinary Chronic Kidney Disease (CKD) Clinics" that was held on April 24, 2015, in Montreal, Quebec. SOURCES OF INFORMATION: The panel consisted of a variety of members from across Canada including a multidisciplinary CKD clinic patient (Randall Russell), nephrology fellow (Dr. David Collister), geriatrician (Dr. Josee Verdon), and nephrologists (Dr. Monica Beaulieu, Dr. Adeera Levin). FINDINGS: The objectives of the symposium were (1) to gain an understanding of the goals of care for CKD patients, (2) to gain an appreciation of different perspectives regarding optimal care for patients with CKD, (3) to examine the components required for optimal care including education strategies, structures, and tools, and (4) to describe a framework and metrics for CKD care which respect patient and system needs. This article summarizes the key concepts discussed at the symposium from a patient and physician perspectives. Key messages include (1) understanding patient values and preferences is important as it provides a framework as to what to prioritize in multidisciplinary CKD clinic and provincial renal program models, (2) barriers to effective communication and education are common in the elderly, and adaptive strategies to limit their influence are critical to improve adherence and facilitate shared decision-making, (3) the use of standardized operating procedures (SOPs) improves efficiency and minimizes practice variability among health care practitioners, and (4) CKD scorecards with standardized system processes are useful in approaching variability as well as measuring and improving patient outcomes. LIMITATIONS: The perspectives provided may not be applicable across centers given the differences in patient populations including age, ethnicity, culture, language, socioeconomic status, education, and multidisciplinary CKD clinic structure and function. IMPLICATIONS: Knowledge transmission by collaborative interprovincial and interprofessional networks may play a role in facilitating optimal CKD care. Validation of system and clinic models that improve outcomes is needed prior to disseminating these best practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".